Chap 6 Function Define a function, which can reuse a piece of code, just with a few different values.

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1 Chap 6 Function Define a function, which can reuse a piece of code, just with a few different values. def tax(bill): """Adds 8% tax to a restaurant bill.""" bill *= 1.08 print "With tax: %f" % bill return bill def tip(bill): """Adds 15% tip to a restaurant bill.""" bill *= 1.15 print "With tip: %f" % bill return bill meal_cost = 100 meal_with_tax = tax(meal_cost) meal_with_tip = tip(meal_with_tax) # With tax: # With tip: Function Junction Function are defined with 3 components: 01. Header, which includes the def keyword, the name of the function, and any parameters the function requires. def hello_world(): # there are no parameters

2 02. Comment explains what the function does. """Prints 'Hello World!' to the console.""" 03. Body, which describes the procedures the function carries out, is indented, just like for conditional statements. def hello_world(): """Prints 'Hello World!' to the console.""" print "Hello World!" Call and Response After defining a function, it must be called to be implemented. This told the program to look for the function name and execute the code inside. def square(n): """Returns the square of a number.""" squared = n**2 print "%d squared is %d." % (n, squared) return squared square(10) # call the function with a parameter # 10 squared is 100 Parameters and Arguments def square(n): n is a parameter of square function. A parameter acts as a variable name for a passed in argument. When the function was called, n holds the value 10. A function can requires many parameters, but when call the function, should generally pass in a matching number of arguments. def power(base, exponent): # Add your parameters here! result = base**exponent

3 print "%d to the power of %d is %d." % (base, exponent, result) power(37,4) # call the function with two parameters passing in its matching positions # 37 to the power of 4 is Functions Calling Functions A function can call another function: def fun_one(n): return n * 5 def fun_two(m): return fun_one(m) + 7 # call a function in another function Practice Makes Perfect The structure of a function: def shout(phrase): if phrase == phrase.upper(): return YOU RE SHOUTING! return Can you speak up? shout( I M INTERESTED IN SHOUTING ) def cube(number): return number * number * number def by_three(number): if cube(number) % 3 == 0:

4 return cube(number) return False Importing a module A module is a file that contains definitions including variables and functions that you can use once it is imported. Python already knows about square roots: print sqrt(25) # get an error message Generic Imports Python said: NameError: name sqrt is not defined. Python doesn t know what square roots are yet. There is a Python module named math that includes a number of useful variables and functions, and sqrt() is one of those functions. In order to access the math module, use import keyword. When you simply import a module this way, it s called a generic import. import math print math.sqrt(25) # equals five Insert math. before sqrt() so that it has the form math.sqrt(). This tells Python not only to import math, but to get the sqrt() function from within math. Function Imports It s possible to import only certain variables or functions from a given module. Pulling in just a single function from a module is called a function import, and it s done with the from keyword: from module import function Now can just type sqrt() to get the square root of a number no more math.sqrt(). from math import sqrt # no need for () after sqrt, just the function name Universal Imports If still want all of the variables and functions in a module but don t want to have to constantly type math. Universal import can handle this.

5 Syntax: from module import * Disadvantages of Universal Import Universal imports fill your program with a ton of variable and function names without the safety or those names still being associated with the module(s) they came from. 01. If you have a function of your very own named sqrt and you import math, your function is safe: there is your sqrt and there is math.sqrt. 02. While if you do from math import *, however, you encounter a problem: namely, two different functions with the exact same name. 03. Even if your won definitions don t directly conflict with names from imported modules, if you import * from several modules at once, you won t be able to figure out which variable or function came from where. Thus, it s best to stick with either import module and type module.name or just import specific variables and functions from various modules as needed. import math everything = dir(math) print everything # Imports the math module # Sets everything to a list of things from math # Prints 'em all! [' doc ', ' name ', ' package ', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'] On Beyond Strings Some functions that are built in to Python (no modules required!). Some of the built-in functions we ve used with strings, such as.upper(),.lower(), str(), and len(). These are great for doing work with strings. def biggest_number(*args): print max(args)

6 return max(args) def smallest_number(*args): print min(args) return min(args) def distance_from_zero(arg): print abs(arg) return abs(arg) biggest_number(-10, -5, 5, 10) smallest_number(-10, -5, 5, 10) distance_from_zero(-10) max() The max() function takes any number of arguments and returns the largest one. Largest can be odd definitions here, so it s best to use max() on integers and floats, where the results are straightforward, and not on other objects, like strings. maximum = max(1992, 3, 18, 22.11) print maximum # returns the largest number in the set of arguments 1992 min() min() returns the smallest of a given series of arguments. minimum = min(1992, 3, 18, 22.11) print minimum # returns the smallest number in the set of arguments 3 abs() abs() function returns the absolute value of the number it takes as an argument that is, that number s distance from 0 on an imagined number line. absolute = abs(-42) print absolute # returns the absolute value 42

7 type() type() function returns the type of the data it receives as an argument. print type(23), type(22.11), type( Yingxie ) # outputs <type int >, <type float >,<type str > Review: Functions def speak(message): return message if happy(): speak("i'm happy!") elif sad(): speak("i'm sad.") speak("i don't know what I'm feeling.") def shut_down(s): if s == 'yes': return "Shutting down" elif s == 'no': return "Shutdown aborted" return "Sorry" shut_down('no')

8 Review: Modules from math import pi, log, sqrt print log(10) # print pi # print sqrt(13689) # Review: Build-in Functions def distance_from_zero(num): if type(num) == int or type(num) == float: return abs(num) return 'Nope' print distance_from_zero(-10) # 10 Chap 6 Exercise: Taking a Vocation Review functions in Python quickly: def bigger(first, second): # has two arguments print max(first, second) # prints out the larger of the two arugments using the built-in function max() return True # the function returns True def answer(): return 42 Planning Your Trip A refresher in how functions are defined. def wages(hours):

9 # If I make $8.35/hour... return 8.35 * hours def hotel_cost(nights): return 140 * nights Getting There An example of defining a function: def fruit_color(fruit): # one argument if fruit == "apple": return "red" elif fruit == "banana": return "yellow" elif fruit == "pear": return "green" def plane_ride_cost(city): if city == 'Charlotte': return 183 elif city == 'Tampa': return 220 elif city == 'Pittsburgh': return 222 # city == 'Los Angeles'

10 return 475 Transportation <sample> check if def finish_game(score): tickets = 10 * score if score >= 10: tickets += 50 elif score >= 7: tickets += 20 return tickets Need a rental car for you to get around. def rental_car_cost(days): rent = 40 if days >=7: return rent * days - 50 elif days >=3: return rent * days - 20 return rent * days print rental_car_cost(2), rental_car_cost(4), rental_car_cost(9) #

11 Pull it Together <sample> Call functions in another function def double(n): return 2 * n def triple(p): return 3 * p def add(a, b): return double(a) + triple(b) # first define two simple functions, then recall them in another function # call double(n) with the new variable a def trip_cost(city, days): return rental_car_cost(days) + plane_ride_cost(city) + hotel_cost(days) print trip_cost('',1) # city == Los Angeles and days == 1, that is =655 Explanation: the number of nights stay in the hotel will always be the same as the number of days rent the car, pass the value of days to the new function in the argument nights Extra expense Still needs to be room for additional costs like fancy food or souvenirs. def trip_cost(city, days, spending_money): return rental_car_cost(days) + plane_ride_cost(city) + hotel_cost(days) + spending_money print trip_cost('',1,200)

12 def hotel_cost(nights): return 140 * nights def plane_ride_cost(city): if city == 'Charlotte': return 183 elif city == 'Tampa': return 220 elif city == 'Pittsburgh': return 222 # city == 'Los Angeles' return 475 def rental_car_cost(days): rent = 40 if days >=7: return rent * days - 50 elif days >=3: return rent * days - 20 return rent * days def trip_cost(city, days, spending_money): return rental_car_cost(days) + plane_ride_cost(city) + hotel_cost(days) + spending_money print trip_cost('',5,600) # examine: calculate the cost of a trip to Los Angeles for 5 days with an extra $600 spending money